The paper introduces a token‑level extension of Omni‑Temporal Classification (OTC) for automatic speech recognition, allowing unsupported tokens to be bypassed while preserving supervision for the rest of the word. Across 19 languages and three corpora, this token‑level OTC consistently outperforms standard CTC, achieving the lowest mean word error rate on every dataset and a 9.45% average relative WER reduction. A predictive‑entropy‑indexed schedule replaces epoch‑based relaxation, reducing training‑length dependence while maintaining performance.
By Saurabh Kumar, Diptiman Mohanta, Prasanta Kumar Ghosh
The paper presents a method to automatically identify utterances in child-centered daylong audio recordings that can be reliably transcribed by modern ASR systems, enabling accurate transcription of a substantial portion of the speech. On four English corpora, the approach achieves a median WER of 0% and a mean WER of 16% when transcribing 30% of the total speech, compared to a median WER of 52% when transcribing all speech. Word frequency distributions from the automatic transcripts correlate strongly with manual annotations (r = 0.94 overall, r = 0.99 for frequent words).
By Daniil Kocharov, Azarias Galama, Okko R\"as\"anen
The paper introduces a unified phoneme‑based TTS‑to‑ASR augmentation pipeline that uses a multilingual TTS model with language‑ID conditioning and incorporates grapheme‑to‑phoneme conversion, reference‑speech filtering, and candidate‑text selection. It proposes phoneme‑frequency‑guided selection (PFGS) to rank sentences based on phoneme frequencies from real ASR labels, and demonstrates that random augmentation and PFGS both improve ASR performance across Arabic, French, Italian, and Portuguese test sets, with PFGS yielding up to a 19.3% relative WER reduction. The study also shows that filtering reference speech can further lower WER by up to 0.59 points on certain datasets.
By Zhen Wang, TianRui Wu, RongQi Han, Hao Wu, Wei Liang
PTC-Bias is a two-stage framework that improves contextual biasing in speech large language models by using phoneme-level temporal competition. In the first stage, PTC Retrieval performs frame-synchronous phoneme decoding to generate a compact shortlist of bias words and their speech intervals. The second stage, PTC Correction, applies a local competition between retrieved candidates and mismatched transcript spans within those intervals, reducing near-homophone and word-segmentation errors without extra SpeechLLM passes. Experiments on LibriSpeech demonstrate consistent gains across two SpeechLLMs, with PTC-Bias reducing B-WER by up to 23.9% relative to CTC-Filter while keeping U-WER nearly unchanged.
By Zhiqi Ai, Han Cheng, Shiyi Mu, Yongjin Zhou, Shugong Xu
The paper introduces a cause-aware error recovery framework for cascaded Automatic Speech Recognition – Large Language Model (ASR‑LLM) pipelines in Spoken Dialogue Systems. It replaces simple ASR confidence filtering with precision‑focused detectors that use deep ASR latent representations to classify token‑level errors into perception, comprehension, and deletion failures. This fine‑grained diagnosis enables the LLM to execute targeted, multi‑turn clarification strategies, leading to a more than two‑fold increase in recall on domain‑shift errors and significant reductions in word error rate and downstream task errors across varied accents, distortions, and domains.
By Yizhou Peng, Ziyang Ma, Changsong Liu, Yi-Wen Chao, Xie Chen, Eng Siong Chng
arXiv:2606. 10213v1 Announce Type: cross Abstract: Speech sound disorders affect approximately 44% of Korean pediatric communication disorder cases, yet automated assessment tools for Korean toddler speech remain underdeveloped.
By Diane Myung-kyung Woodbridge, Jee Hyun Suh
arXiv:2606. 07030v1 Announce Type: cross Abstract: We analyse error patterns of raw waveform acoustic models on TIMIT phone recognition beyond the overall phone error rate (PER).
By Erfan Loweimi, Zhengjun Yue, Andrea Carmantini, Zoran Cvetkovic, Steve Renals, Peter Bell
arXiv:2510.22172v2 Announce Type: replace-cross
Abstract: The Continuous Integrate-and-Fire (CIF) mechanism provides effective alignment for non-autoregressive (NAR) speech recognition. This mechanis...
By Ruixiang Mao, Xiangnan Ma, Qing Yang, Ziming Zhu, Yucheng Qiao, Yuan Ge, Tong Xiao, Shengxiang Gao, Zhengtao Yu, Jingbo Zhu
The paper examines why speech‑based screening for Alzheimer’s disease fails to generalize across different languages, tasks, and recording protocols. Using a leave‑one‑corpus‑out evaluation on four datasets, it finds that 59 of 70 interpretable speech features show conflicting patterns between healthy controls and cognitive risk groups, with pause, silence, and speech rate being highly protocol‑sensitive. The authors propose a fusion method that combines XLM‑R text baseline scores with evidence anchors, improving mean speaker AUC to 0.785 and worst‑case AUC to 0.615, and emphasize the importance of auditing feature transferability and reporting worst‑case domain robustness.
By Zijian Lu, Sizhe Liu, Yin Zhang, Jixuan Deng, Xinrong Lin, Xinchen Yuan, Chicheng Jin, Yiping Zuo, Yuanchao Li
arXiv:2606. 01134v1 Announce Type: cross Abstract: Automatically distinguishing child-directed speech from adult-directed speech in long-form recordings is key to scalable analyses of children's language environments.
By Th\'eo Charlot, Tarek Kunze, Kaveri K. Sheth, Alejandrina Cristia, Marvin Lavechin
arXiv:2606. 17826v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) in non-English clinical settings is challenged by multiscript variability, where the same term may appear in multiple valid orthographic forms.
By Jean Seo, Minkyu Kim, Jeonguk Lee, Jisoo Jung, Wooseok Han, Eunho Yang
arXiv:2607. 12468v1 Announce Type: cross Abstract: We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (WavLM-Large) segmentation, CAM++ embedding-based two-speaker clustering, and a LoRA-adapted omniASR LLM 7B v2 recognizer, with no oracle segmentation or speaker labels at test time.
By Shuming Fang, Shuifei Zeng